TY - JOUR
T1 - An iPhone application using a novel stool color detection algorithm for biliary atresia screening
AU - Hoshino, Eri
AU - Hayashi, Kuniyoshi
AU - Suzuki, Mitsuyoshi
AU - Obatake, Masayuki
AU - Urayama, Kevin Y.
AU - Nakano, Satoshi
AU - Taura, Yasuyuki
AU - Nio, Masaki
AU - Takahashi, Osamu
N1 - Funding Information:
Acknowledgements We wish to thank Dr. Yusuke Yamane, Ms. Saeko Hishinuma, and Dr. Saeko Hirai for their professional advice. We also wish to thank Takashi Taguchi and Shinsuke Ito at UNLOG K.K. (Tokyo) for their substantial cooperation in developing Baby Poop. In addition, we wish to acknowledge the biliary atresia patients’ community (BA no kodomowo mamorukai) for their generous support of this project.
Funding Information:
Funding source This work was supported by Japan Society for the Promotion of Science KAKENHI Grant number 16K19177.
Publisher Copyright:
© 2017, Springer-Verlag GmbH Germany.
PY - 2017/10/1
Y1 - 2017/10/1
N2 - Background: The stool color card has been the primary tool for identifying acholic stools in infants with biliary atresia (BA), in several countries. However, BA stools are not always acholic, as obliteration of the bile duct occurs gradually. This study aims to introduce Baby Poop (Baby unchi in Japanese), a free iPhone application, employing a detection algorithm to capture subtle differences in colors, even with non-acholic BA stools. Methods: The application is designed for use by caregivers of infants aged approximately 2 weeks–1 month. Baseline analysis to determine optimal color parameters predicting BA stools was performed using logistic regression (n = 50). Pattern recognition and machine learning processes were performed using 30 BA and 34 non-BA images. Additional 5 BA and 35 non-BA pictures were used to test accuracy. Results: Hue, saturation, and value (HSV) were the preferred parameter for BA stool identification. A sensitivity and specificity were 100% (95% confidence interval 0.48–1.00 and 0.90–1.00, respectively) even among a collection of visually non-acholic, i.e., pigmented BA stools and relatively pale-colored non-BA stools. Conclusions: Results suggest that an iPhone mobile application integrated with a detection algorithm is an effective and convenient modality for early detection of BA, and potentially for other related diseases.
AB - Background: The stool color card has been the primary tool for identifying acholic stools in infants with biliary atresia (BA), in several countries. However, BA stools are not always acholic, as obliteration of the bile duct occurs gradually. This study aims to introduce Baby Poop (Baby unchi in Japanese), a free iPhone application, employing a detection algorithm to capture subtle differences in colors, even with non-acholic BA stools. Methods: The application is designed for use by caregivers of infants aged approximately 2 weeks–1 month. Baseline analysis to determine optimal color parameters predicting BA stools was performed using logistic regression (n = 50). Pattern recognition and machine learning processes were performed using 30 BA and 34 non-BA images. Additional 5 BA and 35 non-BA pictures were used to test accuracy. Results: Hue, saturation, and value (HSV) were the preferred parameter for BA stool identification. A sensitivity and specificity were 100% (95% confidence interval 0.48–1.00 and 0.90–1.00, respectively) even among a collection of visually non-acholic, i.e., pigmented BA stools and relatively pale-colored non-BA stools. Conclusions: Results suggest that an iPhone mobile application integrated with a detection algorithm is an effective and convenient modality for early detection of BA, and potentially for other related diseases.
KW - Biliary atresia
KW - Detection algorithm
KW - Screening
KW - Stool color
KW - iPhone application
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U2 - 10.1007/s00383-017-4146-8
DO - 10.1007/s00383-017-4146-8
M3 - Article
C2 - 28819683
AN - SCOPUS:85027718424
SN - 0179-0358
VL - 33
SP - 1115
EP - 1121
JO - Pediatric Surgery International
JF - Pediatric Surgery International
IS - 10
ER -